# Domain Adaptive Transfer Learning for Fault Diagnosis

arXiv: https://arxiv.org/abs/1905.06004 · HTML: https://mlsanity.com/paper/1905.06004/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2019.

## Built on by (most cited first, 11 of 11)

- [DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices](https://mlsanity.com/paper/2303.13325/) (2023)
- [Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask Learning](https://mlsanity.com/paper/2204.12404/) (2022)
- [Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction](https://mlsanity.com/paper/2302.01704/) (2023)
- [Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regression](https://mlsanity.com/paper/2401.13721/) (2024)
- [Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions](https://mlsanity.com/paper/2406.06607/) (2024)
- [Addressing distributional shifts in operations management: The case of order fulfillment in customized production](https://mlsanity.com/paper/2304.11910/) (2023)
- [Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry](https://mlsanity.com/paper/2112.07356/) (2021)
- [Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios](https://mlsanity.com/paper/2307.01429/) (2023)
- [Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis](https://mlsanity.com/paper/2312.02826/) (2023)
- [Asymmetric Adaptation-based Real-time Fault Diagnosis Under Transitional Operating Conditions](https://mlsanity.com/paper/2605.24457/) (2026)
- [Robust Fault Detection in Mechanical Multimodal Time Series via Self-Supervised Cross-Modal Reconstruction](https://mlsanity.com/paper/2609.16314/) (2026)

## Abstract (arXiv)

Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models fromone machine to the other has raised great interest. Solving these domain adaptive transfer learning tasks has the potential to save large efforts on manually labeling data and modifying models for new machines in the same fleet. Although data-driven methods have shown great potential in fault diagnosis applications, their ability to generalize on new machines and new working conditions are limited because of their tendency to overfit to the training set in reality. One promising solution to this problem is to use domain adaptation techniques. It aims to improve model performance on the target new machine. Inspired by its successful implementation in computer vision, we introduced Domain-Adversarial Neural Networks (DANN) to our context, along with two other popular methods existing in previous fault diagnosis research. We then carefully justify the applicability of these methods in realistic fault diagnosis settings, and offer a unified experimental protocol for a fair comparison between domain adaptation methods for fault diagnosis problems.